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In the past several years, a number of programs that estimate latent class models have appeared. These programs, based on maximum-likelihood estimation, have clearly superceded the earliest programs for latent class analysis: MLLSA (maximum likelihood latent structure analysis) by Clifford Clogg, LCAG (latent classes according to Goodman) by Jacques Hagenaars and Ruud Luijkx, and LAT and Newton by Shelby Haberman. The programs listed herein are a selection of some of the better-known programs that are currently available for solving a variety of latent class models.
WINMIRA is a Windows-based program that estimates and tests a wide variety of discrete mixture models for categorical variables, including models with both nominal and continuous latent variables. In addition to latent class models, this program can be used to estimate Rasch models and mixed Rasch models with dichotomous and polytomous data.
PANMARK estimates and tests a range of latent class models, although as its name – a combination of panel Markov models – implies, this program was developed to estimate longitudinal latent class models. PANMARK also has a number of features such as its ability to generate multiple sets of starting values, which may be useful in avoiding local maxima, its reporting of asymptotic standard errors for model parameter estimates, and its use of bootstrapping methods for comparing models with different numbers of latent classes.
LatentGOLD is a Windows-based latent class analysis program that is compatible with SPSS.
Latent class analysis is frequently used when the researcher has a set of categorically scored observed measures that are highly interrelated. The latent class model (LCM) – which is often characterized as the categorical data analog to factor analysis – is most appropriately used when the observed indicator variables are associated because of some underlying unobserved factor rather than being causally related. For example, the correctness (incorrectness) of answers to questions on an exam may be highly interrelated as a result of mastery; those who have mastered the material will tend to answer items correctly, and those who have yet to master the material will tend to answer them incorrectly. Thus, in a sufficiently large sampling of student exams, we would anticipate that those who correctly answered question 1 would also be more likely to have correctly answered questions 2, 3, and so forth, yielding a clear association among the “variables” (exam questions). Frequent instances of this kind of association can be found in the social and behavioral sciences (e.g., self-esteem, religiosity, partisan identification, consumer loyalty).
Since the early 1990s, the LCM has emerged as a powerful new method for the analysis of categorically scored data. As is clear from the range of applications in this volume, the range of topics to which the LCM can be fruitfully applied is quite broad.
This appendix lists a number of books, chapters, and articles that may prove useful to those who wish to learn more about latent class analysis. Although the listing is by no means exhaustive, an effort has been made to include many of the most widely known sources. We have selected a set of general headings with which to separate the various works. Because several of the works can be classified under multiple topics, however, the reader is advised to consider works listed under headings other than that of his or her immediate interest. The bibliographies of the individual contributions are also an excellent source of additional readings on specific topics related to the latent class model.
Applied Latent Class Analysis introduces several innovations in latent class analysis to a wider audience of researchers. Many of the world's leading innovators in the field of latent class analysis contributed essays to this volume, each presenting a key innovation to the basic latent class model and illustrating how it can prove useful in situations typically encountered in actual research.
In two very important overviews of latent class modeling, Clifford Clogg discussed the advances made in the area of latent class analysis during the past two decades (Clogg, 1981, 1995). From a formal, statistical point of view, great progress has been made regarding the estimation and testing of latent class models. It also has become clear that particular developments in econometrics, biometrics, and mathematical statistics concerning (finite) mixture models, unobserved heterogeneity, frailty models, and random coefficient models are identical or at least have very close ties to latent class modeling, thus enhancing our insight into the potentialities of latent class analysis. Furthermore, in the social and behavioral sciences, close relationships between latent class and loglinear models and between latent class and latent trait (item response) models have been discovered, leading latent class analysis to be viewed as a very general latent variable model for categorical data. Finally, and perhaps most importantly, it has been shown that latent class analysis provides a very useful tool for answering many substantive questions in the social and behavioral sciences.
Nevertheless, and despite the present availability of user-friendly software with which latent class models can be easily and routinely applied, practicing social and behavioral researchers do not always consider latent class analysis a serious alternative for better-known techniques, such as factor analysis or linear structural equation modeling, even where it would be a more appropriate means to address their questions.
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